AI Computer Vision in Food & Beverage Industry
- Shubham Darwatkar
- Aug 7
- 2 min read
Keeping Quality Consistent in High-Speed Production
Walk into a supermarket, and you’ll see neatly stacked trays of ready-to-eat meals - each portion looking identical, every seal seemingly flawless. But behind that consistency is a challenge most of us never think about: how do food producers keep every single tray perfect when production lines are flying at up to 150 trays a minute?
The truth is, even a small slip, a cracked container, a loose seal, or a stray contaminant can mean regulatory backlash, customer distrust, and multimillion-dollar recalls. Manual inspection alone simply can’t keep up anymore. That’s where Eaglai Detect comes in.
The Challenge in the Food & Beverage Industry
A large-scale ready-to-eat meal facility was struggling with problems that will sound familiar to anyone in the food business:
Uneven portion sizes and subpar presentation
Loose seals on film lids
Cracked containers
The risk of foreign objects sneaking into trays
Human inspectors overwhelmed by lines running at 100–150 trays per minute
The stakes were high. Defective batches led to recalls and fines, costing the company around $1.7 million every year, excluding the reputational damage.
Implementing AI in Food Industry: Eaglai Detect
The facility deployed Eaglai Detect, an AI-powered computer vision system in food industry designed to handle inspection at industrial speed. Here’s what went into it:
High-resolution cameras capturing trays from the top and sides
Thermal imaging modules checking seal integrity
AI models trained to spot:
Inconsistent portion sizes
Burnt or undercooked sections
Seal leaks and tray deformities
Foreign objects that don’t belong in food
Automated rejection of defective trays in real time
Dashboards for traceability, ensuring compliance across seven production lines
How It Worked | Operational Flow of the AI System
Each tray was analyzed in under 100 milliseconds
AI feedback loops adjusted portioning machines mid-batch
Centralized dashboards logged inspection data for traceability
The Results
The difference was dramatic:
Detection accuracy jumped to ~98.5% (from ~82%)
Contaminant detection soared above 95% (manual inspection had barely hit 50%)
Inspection time fell to under 0.2 seconds per tray (down from 3 seconds)
Annual recall costs dropped from $1.7 million to under $400,000
The deployment improved presentation, boosted retailer confidence, and delivered full ROI in ~11 months
The Takeaway
AI vision in F&B isn’t just about preventing recalls. It’s about delivering safe, consistent, and visually appealing products that consumers trust and retailers want.
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